This invention relates generally to tomographic imaging and more particularly, to reducing gain fluctuation in image reconstruction.
Imaging technology, for example, but not limited to computed tomographic (CT) scanning technology permits a non-invasive technique to obtain internal images of the human body for medical diagnostic purposes. In clinical practice, it is common for CT scans to results in data sets with truncated projections. Data truncation occurs when the patient, the CT table, or anything else placed in the bore of the scanner, extends beyond the scan field-of-view. This situation may arise for large patients, or when centering the desired patient anatomy at the center of the field-of-view. Even in situations where both the patient and the table are carefully positioned within the scan field-of-view, the table slicker often used to protect the CT table mechanism from bodily fluids may be left hanging off the sides of the table, and may extend outside the scan field of view. It is also possible for tubes of contrast liquid, intravenous (IV) fluids, or other medical accessories to be present outside the scan field-of-view.
CT imaging generally includes data acquisition, followed by a sequence of pre-processing corrections, before image reconstruction is performed to generate the patient images. Raw projection data is processed prior to image reconstruction by applying scanner-specific corrections and calibrations. One of the first steps is the reference normalization step, which addresses the impact of fluctuations in the x-ray tube current output on the projections. For this purpose, a set of reference channels is placed slightly outside the scan field-of-view, to measure x-ray photons directly from the x-ray tube without attenuation by the scanned object. Coefficients calculated from these channels monitor the x-ray flux and are used to normalize the projections relative to one another. When an object is present outside the scan field-of-view, however, the reference channels are blocked, and pre-processing cannot accurately estimate the correction coefficients. This and other steps in pre-correction modify the acquired projection data and may result in inaccurate projection measurements resulting in image artifacts.
Other sources of error may be present that could also result in a view dependent DC bias in the pre-processed data. For example, the gain of the detector cell can be angularly dependent as a result of the gravitational force on the collimator plates, which leads to variations in the shadow generated by these plates.
Image reconstruction denotes the process of generating images from the pre-corrected projection data. In practice, analytic techniques such as the Filtered Back-Projection algorithm (FBP) are typically used. Iterative reconstruction (IR) algorithms have also been more recently introduced for CT and offer the potential for significantly improved image quality over conventional FBP and other direct techniques. Pre-processing corrections and calibrations are tuned to minimize image artifacts for FBP-like algorithms and reconstruct an accurate map of CT numbers. However, using the same pre-processed data to initialize iterative reconstruction may result in significant image artifacts in cases where projection data is truncated.